Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—Winter is a defining season for Canada, and recreational ice fishing has cultural and economic value to Canadians. However, winter remains a poorly studied season for Canada’s freshwater fish and fisheries. Despite its historical oversight, winter ecology has received a recent surge of interest, likely motivated by shorter, warmer winters arising under a globally changing climate. This chapter draws from existing literature to explore how winter influences freshwater fishes and fisheries as well as how climate change might affect these relationships. Habitat characteristics in lakes and streams change dramatically during winter, with light, temperature, and oxygen all declining to various degrees depending on the system. Species respond differently to this abiotic variation with respect to their habitat use and activity, which appears closely tied to thermal preference. Whether species reproduce before, during, or after winter further shapes fish behavioral and energetic responses to winter. Recreational ice fishing, natural resource extraction, water drawdown, and salinization are all anthropogenic activities occurring during winter with potentially negative effects on fish. But climate-driven reductions in winter duration remain the largest uncertainty for Canadian freshwater fishes. Warmer winters with less ice cover will increase the growing season but also increase metabolic costs and potentially decrease fish growth, reproductive output, and survival. We provide a framework for motivating future studies to consider how conditions before and during winter carry over to influence growth and reproduction after winter in different populations and species. Continued winter-based research will enhance understanding of fish ecology and help inform management about the potential positive versus negative outcomes of shorter, warmer winters for Canadian freshwater fishes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".